8 agentic coding trends shaping software engineering in 2026, according to Anthropic

Software teams are under pressure to ship faster, but most AI coding tools still handle only narrow slices of the job rather than full builds — a pattern the next wave of agent systems is expected to change, according to Anthropic’s 2026 Agentic Coding Trends Report.

The report examines how AI coding agents are starting to move beyond one-off assistance into broader implementation roles across the software development lifecycle. Based on customer deployments and internal research, Anthropic identifies eight trends that it says will shape how engineering work is organised over the coming year.

1. A ‘tectonic shift’ for the software development lifecycle

The foundational trend identified by Anthropic is one that will reorganise how software is built and maintained, with engineering roles shifting more toward agent supervision, system design, and output review.

The report contends that AI agents are shifting coding from hands-on implementation toward agent direction and review. Agents increasingly handle writing, testing, debugging, and documentation, while engineers focus on architecture and decision-making. Onboarding time to new codebases drops sharply, allowing more flexible staffing and faster project starts, with humans still responsible for judgment and verification.

2. Agents become team players

Organisations are moving from single coding agents to groups of specialised agents working in parallel under an orchestrator. Each agent handles a defined role, which improves speed and task coverage. This requires better task breakdown, coordination methods, and visibility across concurrent agent sessions and code contributions.

3. Agents go end-to-end

Agents are progressing from short, one-off tasks to work that continues for hours or days. The report expects agents to plan, iterate, recover from errors, and maintain project context across long runs. This makes full application builds, backlog cleanup, and technical debt reduction more feasible, with humans checking progress at key points.

4. Agents learn when to ask for help

Developers delegate only a limited share of work fully to AI today, but agent behaviour is starting to change. Newer systems can detect uncertainty, flag risk, and request human input at key decision points rather than attempting every task end to end.

Agents are also increasingly used to review AI-generated code for security issues, consistency, and defects at a scale humans cannot match. The effect is more selective human review focused on judgment-heavy calls.

5. Agents spread beyond software engineers

Early agentic coding tools focused on helping professional engineers work faster inside standard development environments. However, agent support is expanding into legacy and niche languages, including COBOL and Fortran, making older systems easier to maintain. New interfaces are also opening these tools to non-traditional developers in security, operations, design, and data roles, allowing more teams to build small automations and internal tools directly.

6. More code, shorter timelines

Agent use is expected to change both the cost and pace of software projects. As agent capability, coordination methods, and human oversight improve together, delivery timelines fall and more projects become practical to pursue. Work that once took weeks can be done in days, which shifts which ideas get funded and built.

Lower delivery effort and higher output also reduce overall project cost per feature shipped.

7. Non-engineers embrace agentic coding

If trend 5 was about agentic coding spreading across more technical users and environments, trend 7 tracks its foray into wider business departments, where domain experts are starting to build small tools and automations themselves.

Sales, legal, marketing, and operations teams are using agents to solve local process problems without waiting on engineering queues. That shifts some solution-building away from central development teams toward the people closest to the problem.

8. Agents help defenders — and attackers — scale

Agentic coding tools are a double-edged sword for security, improving defensive work while lowering the barrier for offensive use.

Engineers can run deeper code reviews, hardening checks, and monitoring with agent support that previously required specialist skills. But that same scaling effect also applies to attackers, who can use agents to speed up reconnaissance and exploit development. That puts more weight on building security controls into systems early and using automated defence tools to keep pace with automated threats.

Tools of the agentic coding trade

Across the eight trends identified by Anthropic, agents are starting to take on larger chunks of implementation, working for longer stretches, coordinating with other agents, and reaching users far beyond core engineering teams. This changes how software gets built, who builds it, and how projects are staffed and secured. Recent product launches and model updates underline the same pattern, with the likes of OpenAI recently launching a new desktop app for supervising coding agents, while both Anthropic and OpenAI last week upgraded their flagship LLMs with longer-duration AI work firmly in their crosshairs.

Of course, this all raises questions about what happens to software jobs, with industry analysis pointing to more automated execution alongside rising demand for engineers who can scope problems, supervise agent output, and make high-stakes technical calls. That shift also creates space for agent enablement platforms such as Tessl, which focus on structuring, supervising, and governing agent-led software work.

Anthropic’s report describes the same role adjustment: more time spent on orchestration, review, and system design, and less on routine implementation. Agents take on more of the build-and-fix cycle, while engineers concentrate on direction, judgment, and oversight. Software engineering stays central, but the day-to-day emphasis is changing.